Paper Info
Title
Distributed consensus-based Bayesian estimation: sufficient conditions for performance characterization
Abstract
The paper considers the framework of distributed Bayesian linear estimation. We introduce some consensus-based estimation strategies that are equivalent to centralized ones pending knowledge of some parameters, e.g. number of agents in the network. If such parameters are not known, agents can estimate them locally or exploit prior knowledge. We show that in this case the performance depends on parameter uncertainty in such a way that, in case of large errors, the distributed estimator can perform worse than the local one. Then, we find some sufficient conditions on the error magnitude which ensure that the distributed scheme behaves better than the local one.
Year
DOI
Venue
2010
10.1109/ACC.2010.5531213
American Control Conference
Keywords
DocType
ISSN
bayes methods,distributed control,distributed parameter systems,estimation theory,uncertain systems,consensus based estimation strategy,distributed bayesian linear estimation,distributed estimator,error magnitude,parameter uncertainty,performance characterization,bayesian linear model,consensus,distributed estimation,sufficient conditions,bayesian methods,distributed consensus,noise,measurement uncertainty,harmonic analysis,linear model,noise measurement
Conference
0743-1619
ISBN
Citations 
PageRank 
978-1-4244-7426-4
4
0.45
References 
Authors
8
3
Name
Order
Citations
PageRank
Damiano Varagnolo151.48
Pillonetto Gianluigi287780.84
Luca Schenato3118894.99